Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory380.5 B

Variable types

Numeric18
Categorical8

Alerts

Age is highly overall correlated with Age_NumProducts_Interaction and 1 other fieldsHigh correlation
Age_Balance_Interaction is highly overall correlated with Balance and 6 other fieldsHigh correlation
Age_NumProducts_Interaction is highly overall correlated with Age and 2 other fieldsHigh correlation
Age_Squared is highly overall correlated with Age and 1 other fieldsHigh correlation
Balance is highly overall correlated with Age_Balance_Interaction and 6 other fieldsHigh correlation
BalanceCategory is highly overall correlated with Age_Balance_Interaction and 5 other fieldsHigh correlation
Balance_Squared is highly overall correlated with Age_Balance_Interaction and 6 other fieldsHigh correlation
Balance_to_EstimatedSalary_Ratio is highly overall correlated with Age_Balance_Interaction and 5 other fieldsHigh correlation
CreditScore is highly overall correlated with CreditScore_SquaredHigh correlation
CreditScore_Balance_Interaction is highly overall correlated with Age_Balance_Interaction and 6 other fieldsHigh correlation
CreditScore_EstimatedSalary_Interaction is highly overall correlated with EstimatedSalary and 1 other fieldsHigh correlation
CreditScore_Squared is highly overall correlated with CreditScoreHigh correlation
EstimatedSalary is highly overall correlated with CreditScore_EstimatedSalary_Interaction and 1 other fieldsHigh correlation
Gender is highly overall correlated with RegionChurnRateHigh correlation
Log_Balance is highly overall correlated with Age_Balance_Interaction and 6 other fieldsHigh correlation
Log_EstimatedSalary is highly overall correlated with CreditScore_EstimatedSalary_Interaction and 1 other fieldsHigh correlation
NumOfProducts is highly overall correlated with Age_NumProducts_Interaction and 1 other fieldsHigh correlation
NumProducts_to_Tenure_Ratio is highly overall correlated with Tenure and 1 other fieldsHigh correlation
RegionChurnRate is highly overall correlated with GenderHigh correlation
Tenure is highly overall correlated with NumProducts_to_Tenure_Ratio and 1 other fieldsHigh correlation
Tenure_Balance_Interaction is highly overall correlated with Age_Balance_Interaction and 7 other fieldsHigh correlation
Tenure_NumProducts_Interaction is highly overall correlated with NumOfProducts and 1 other fieldsHigh correlation
Balance_to_EstimatedSalary_Ratio is highly skewed (γ1 = 93.39667598) Skewed
CreditScore_EstimatedSalary_Interaction has unique values Unique
Tenure has 413 (4.1%) zeros Zeros
Balance has 3617 (36.2%) zeros Zeros
Age_Balance_Interaction has 3617 (36.2%) zeros Zeros
CreditScore_Balance_Interaction has 3617 (36.2%) zeros Zeros
Tenure_NumProducts_Interaction has 413 (4.1%) zeros Zeros
Tenure_Balance_Interaction has 3893 (38.9%) zeros Zeros
Balance_Squared has 3617 (36.2%) zeros Zeros
Log_Balance has 3617 (36.2%) zeros Zeros
Balance_to_EstimatedSalary_Ratio has 3617 (36.2%) zeros Zeros

Reproduction

Analysis started2024-12-03 10:08:43.326335
Analysis finished2024-12-03 10:10:03.216453
Duration1 minute and 19.89 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

CreditScore
Real number (ℝ)

High correlation 

Distinct460
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean650.5288
Minimum350
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-12-03T10:10:03.369276image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile489
Q1584
median652
Q3718
95-th percentile812
Maximum850
Range500
Interquartile range (IQR)134

Descriptive statistics

Standard deviation96.653299
Coefficient of variation (CV)0.14857651
Kurtosis-0.42572568
Mean650.5288
Median Absolute Deviation (MAD)67
Skewness-0.071606608
Sum6505288
Variance9341.8602
MonotonicityNot monotonic
2024-12-03T10:10:03.632577image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850 233
 
2.3%
678 63
 
0.6%
655 54
 
0.5%
705 53
 
0.5%
667 53
 
0.5%
684 52
 
0.5%
670 50
 
0.5%
651 50
 
0.5%
660 48
 
0.5%
683 48
 
0.5%
Other values (450) 9296
93.0%
ValueCountFrequency (%)
350 5
0.1%
351 1
 
< 0.1%
358 1
 
< 0.1%
359 1
 
< 0.1%
363 1
 
< 0.1%
365 1
 
< 0.1%
367 1
 
< 0.1%
373 1
 
< 0.1%
376 2
 
< 0.1%
382 1
 
< 0.1%
ValueCountFrequency (%)
850 233
2.3%
849 8
 
0.1%
848 5
 
0.1%
847 6
 
0.1%
846 5
 
0.1%
845 6
 
0.1%
844 7
 
0.1%
843 2
 
< 0.1%
842 7
 
0.1%
841 12
 
0.1%

Geography
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size615.4 KiB
France
5014 
Germany
2509 
Spain
2477 

Length

Max length7
Median length6
Mean length6.0032
Min length5

Characters and Unicode

Total characters60032
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrance
2nd rowSpain
3rd rowFrance
4th rowFrance
5th rowSpain

Common Values

ValueCountFrequency (%)
France 5014
50.1%
Germany 2509
25.1%
Spain 2477
24.8%

Length

2024-12-03T10:10:03.889761image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T10:10:04.106162image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
france 5014
50.1%
germany 2509
25.1%
spain 2477
24.8%

Most occurring characters

ValueCountFrequency (%)
n 10000
16.7%
a 10000
16.7%
r 7523
12.5%
e 7523
12.5%
F 5014
8.4%
c 5014
8.4%
G 2509
 
4.2%
m 2509
 
4.2%
y 2509
 
4.2%
S 2477
 
4.1%
Other values (2) 4954
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 10000
16.7%
a 10000
16.7%
r 7523
12.5%
e 7523
12.5%
F 5014
8.4%
c 5014
8.4%
G 2509
 
4.2%
m 2509
 
4.2%
y 2509
 
4.2%
S 2477
 
4.1%
Other values (2) 4954
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 10000
16.7%
a 10000
16.7%
r 7523
12.5%
e 7523
12.5%
F 5014
8.4%
c 5014
8.4%
G 2509
 
4.2%
m 2509
 
4.2%
y 2509
 
4.2%
S 2477
 
4.1%
Other values (2) 4954
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 10000
16.7%
a 10000
16.7%
r 7523
12.5%
e 7523
12.5%
F 5014
8.4%
c 5014
8.4%
G 2509
 
4.2%
m 2509
 
4.2%
y 2509
 
4.2%
S 2477
 
4.1%
Other values (2) 4954
8.3%

Gender
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size604.7 KiB
Male
5457 
Female
4543 

Length

Max length6
Median length4
Mean length4.9086
Min length4

Characters and Unicode

Total characters49086
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 5457
54.6%
Female 4543
45.4%

Length

2024-12-03T10:10:04.330956image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T10:10:04.516031image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
male 5457
54.6%
female 4543
45.4%

Most occurring characters

ValueCountFrequency (%)
e 14543
29.6%
a 10000
20.4%
l 10000
20.4%
M 5457
 
11.1%
F 4543
 
9.3%
m 4543
 
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49086
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 14543
29.6%
a 10000
20.4%
l 10000
20.4%
M 5457
 
11.1%
F 4543
 
9.3%
m 4543
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49086
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 14543
29.6%
a 10000
20.4%
l 10000
20.4%
M 5457
 
11.1%
F 4543
 
9.3%
m 4543
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49086
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 14543
29.6%
a 10000
20.4%
l 10000
20.4%
M 5457
 
11.1%
F 4543
 
9.3%
m 4543
 
9.3%

Age
Real number (ℝ)

High correlation 

Distinct70
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.9218
Minimum18
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-12-03T10:10:04.725096image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile25
Q132
median37
Q344
95-th percentile60
Maximum92
Range74
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.487806
Coefficient of variation (CV)0.26945841
Kurtosis1.3953471
Mean38.9218
Median Absolute Deviation (MAD)6
Skewness1.0113203
Sum389218
Variance109.99408
MonotonicityNot monotonic
2024-12-03T10:10:05.000395image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 478
 
4.8%
38 477
 
4.8%
35 474
 
4.7%
36 456
 
4.6%
34 447
 
4.5%
33 442
 
4.4%
40 432
 
4.3%
39 423
 
4.2%
32 418
 
4.2%
31 404
 
4.0%
Other values (60) 5549
55.5%
ValueCountFrequency (%)
18 22
 
0.2%
19 27
 
0.3%
20 40
 
0.4%
21 53
 
0.5%
22 84
0.8%
23 99
1.0%
24 132
1.3%
25 154
1.5%
26 200
2.0%
27 209
2.1%
ValueCountFrequency (%)
92 2
 
< 0.1%
88 1
 
< 0.1%
85 1
 
< 0.1%
84 2
 
< 0.1%
83 1
 
< 0.1%
82 1
 
< 0.1%
81 4
< 0.1%
80 3
< 0.1%
79 4
< 0.1%
78 5
0.1%

Tenure
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0128
Minimum0
Maximum10
Zeros413
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-12-03T10:10:05.263006image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8921744
Coefficient of variation (CV)0.57695786
Kurtosis-1.1652252
Mean5.0128
Median Absolute Deviation (MAD)2
Skewness0.010991458
Sum50128
Variance8.3646726
MonotonicityNot monotonic
2024-12-03T10:10:05.463990image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 1048
10.5%
1 1035
10.3%
7 1028
10.3%
8 1025
10.2%
5 1012
10.1%
3 1009
10.1%
4 989
9.9%
9 984
9.8%
6 967
9.7%
10 490
4.9%
ValueCountFrequency (%)
0 413
 
4.1%
1 1035
10.3%
2 1048
10.5%
3 1009
10.1%
4 989
9.9%
5 1012
10.1%
6 967
9.7%
7 1028
10.3%
8 1025
10.2%
9 984
9.8%
ValueCountFrequency (%)
10 490
4.9%
9 984
9.8%
8 1025
10.2%
7 1028
10.3%
6 967
9.7%
5 1012
10.1%
4 989
9.9%
3 1009
10.1%
2 1048
10.5%
1 1035
10.3%

Balance
Real number (ℝ)

High correlation  Zeros 

Distinct6382
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76485.889
Minimum0
Maximum250898.09
Zeros3617
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-12-03T10:10:05.693910image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median97198.54
Q3127644.24
95-th percentile162711.67
Maximum250898.09
Range250898.09
Interquartile range (IQR)127644.24

Descriptive statistics

Standard deviation62397.405
Coefficient of variation (CV)0.81580283
Kurtosis-1.4894118
Mean76485.889
Median Absolute Deviation (MAD)46766.79
Skewness-0.14110871
Sum7.6485889 × 108
Variance3.8934362 × 109
MonotonicityNot monotonic
2024-12-03T10:10:05.974131image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3617
36.2%
130170.82 2
 
< 0.1%
105473.74 2
 
< 0.1%
159397.75 1
 
< 0.1%
144238.7 1
 
< 0.1%
112262.84 1
 
< 0.1%
109106.8 1
 
< 0.1%
142147.32 1
 
< 0.1%
109109.33 1
 
< 0.1%
146587.3 1
 
< 0.1%
Other values (6372) 6372
63.7%
ValueCountFrequency (%)
0 3617
36.2%
3768.69 1
 
< 0.1%
12459.19 1
 
< 0.1%
14262.8 1
 
< 0.1%
16893.59 1
 
< 0.1%
23503.31 1
 
< 0.1%
24043.45 1
 
< 0.1%
27288.43 1
 
< 0.1%
27517.15 1
 
< 0.1%
27755.97 1
 
< 0.1%
ValueCountFrequency (%)
250898.09 1
< 0.1%
238387.56 1
< 0.1%
222267.63 1
< 0.1%
221532.8 1
< 0.1%
216109.88 1
< 0.1%
214346.96 1
< 0.1%
213146.2 1
< 0.1%
212778.2 1
< 0.1%
212696.32 1
< 0.1%
212692.97 1
< 0.1%

NumOfProducts
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
1
5084 
2
4590 
3
 
266
4
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Length

2024-12-03T10:10:06.230200image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T10:10:06.407455image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

HasCrCard
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
1
7055 
0
2945 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Length

2024-12-03T10:10:06.598170image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T10:10:06.770767image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Most occurring characters

ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

IsActiveMember
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
1
5151 
0
4849 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Length

2024-12-03T10:10:06.951735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T10:10:07.119055image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Most occurring characters

ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

EstimatedSalary
Real number (ℝ)

High correlation 

Distinct9999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100090.24
Minimum11.58
Maximum199992.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-12-03T10:10:07.360247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum11.58
5-th percentile9851.8185
Q151002.11
median100193.91
Q3149388.25
95-th percentile190155.38
Maximum199992.48
Range199980.9
Interquartile range (IQR)98386.137

Descriptive statistics

Standard deviation57510.493
Coefficient of variation (CV)0.57458642
Kurtosis-1.1815184
Mean100090.24
Median Absolute Deviation (MAD)49198.15
Skewness0.0020853577
Sum1.0009024 × 109
Variance3.3074568 × 109
MonotonicityNot monotonic
2024-12-03T10:10:07.700669image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24924.92 2
 
< 0.1%
121505.61 1
 
< 0.1%
89874.82 1
 
< 0.1%
72500.68 1
 
< 0.1%
182692.8 1
 
< 0.1%
4993.94 1
 
< 0.1%
124964.82 1
 
< 0.1%
161971.42 1
 
< 0.1%
3729.89 1
 
< 0.1%
55313.44 1
 
< 0.1%
Other values (9989) 9989
99.9%
ValueCountFrequency (%)
11.58 1
< 0.1%
90.07 1
< 0.1%
91.75 1
< 0.1%
96.27 1
< 0.1%
106.67 1
< 0.1%
123.07 1
< 0.1%
142.81 1
< 0.1%
143.34 1
< 0.1%
178.19 1
< 0.1%
216.27 1
< 0.1%
ValueCountFrequency (%)
199992.48 1
< 0.1%
199970.74 1
< 0.1%
199953.33 1
< 0.1%
199929.17 1
< 0.1%
199909.32 1
< 0.1%
199862.75 1
< 0.1%
199857.47 1
< 0.1%
199841.32 1
< 0.1%
199808.1 1
< 0.1%
199805.63 1
< 0.1%

Exited
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
0
7963 
1
2037 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7963
79.6%
1 2037
 
20.4%

Length

2024-12-03T10:10:07.949461image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T10:10:08.114202image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 7963
79.6%
1 2037
 
20.4%

Most occurring characters

ValueCountFrequency (%)
0 7963
79.6%
1 2037
 
20.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7963
79.6%
1 2037
 
20.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7963
79.6%
1 2037
 
20.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7963
79.6%
1 2037
 
20.4%

Age_Balance_Interaction
Real number (ℝ)

High correlation  Zeros 

Distinct6383
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2995492
Minimum0
Maximum13796953
Zeros3617
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-12-03T10:10:09.125711image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3360210.8
Q34911035.1
95-th percentile7308972.6
Maximum13796953
Range13796953
Interquartile range (IQR)4911035.1

Descriptive statistics

Standard deviation2646812.7
Coefficient of variation (CV)0.88359865
Kurtosis-0.68593908
Mean2995492
Median Absolute Deviation (MAD)2508157.5
Skewness0.333125
Sum2.995492 × 1010
Variance7.0056173 × 1012
MonotonicityNot monotonic
2024-12-03T10:10:09.420928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3617
36.2%
4621662.9 2
 
< 0.1%
5718801.16 1
 
< 0.1%
4813426.73 1
 
< 0.1%
4161915 1
 
< 0.1%
3761550.3 1
 
< 0.1%
5100728 1
 
< 0.1%
3750206.2 1
 
< 0.1%
4041462.24 1
 
< 0.1%
3927844.8 1
 
< 0.1%
Other values (6373) 6373
63.7%
ValueCountFrequency (%)
0 3617
36.2%
150747.6 1
 
< 0.1%
499198 1
 
< 0.1%
573122.74 1
 
< 0.1%
709530.78 1
 
< 0.1%
750575.49 1
 
< 0.1%
804923.13 1
 
< 0.1%
829147.68 1
 
< 0.1%
965392.8 1
 
< 0.1%
965929.2 1
 
< 0.1%
ValueCountFrequency (%)
13796952.94 1
< 0.1%
13588090.92 1
< 0.1%
13002612.69 1
< 0.1%
12903905.97 1
< 0.1%
12678403.62 1
< 0.1%
12534373.04 1
< 0.1%
12481756.32 1
< 0.1%
12427644.1 1
< 0.1%
12234455.68 1
< 0.1%
12087895.55 1
< 0.1%

Age_NumProducts_Interaction
Real number (ℝ)

High correlation 

Distinct151
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.371
Minimum18
Maximum272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-12-03T10:10:09.688120image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile28
Q138
median54
Q374
95-th percentile114
Maximum272
Range254
Interquartile range (IQR)36

Descriptive statistics

Standard deviation28.616989
Coefficient of variation (CV)0.48200281
Kurtosis4.2937547
Mean59.371
Median Absolute Deviation (MAD)18
Skewness1.6207495
Sum593710
Variance818.93205
MonotonicityNot monotonic
2024-12-03T10:10:09.940134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 257
 
2.6%
74 254
 
2.5%
38 250
 
2.5%
72 248
 
2.5%
68 246
 
2.5%
35 238
 
2.4%
66 237
 
2.4%
62 234
 
2.3%
76 234
 
2.3%
70 229
 
2.3%
Other values (141) 7573
75.7%
ValueCountFrequency (%)
18 11
 
0.1%
19 17
 
0.2%
20 18
 
0.2%
21 19
 
0.2%
22 43
0.4%
23 47
0.5%
24 67
0.7%
25 63
0.6%
26 98
1.0%
27 93
0.9%
ValueCountFrequency (%)
272 1
 
< 0.1%
244 1
 
< 0.1%
240 2
< 0.1%
232 2
< 0.1%
231 1
 
< 0.1%
228 2
< 0.1%
220 3
< 0.1%
213 1
 
< 0.1%
212 1
 
< 0.1%
210 1
 
< 0.1%

CreditScore_Balance_Interaction
Real number (ℝ)

High correlation  Zeros 

Distinct6384
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49794074
Minimum0
Maximum1.8086147 × 108
Zeros3617
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-12-03T10:10:10.226531image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median59843137
Q382830378
95-th percentile1.1163359 × 108
Maximum1.8086147 × 108
Range1.8086147 × 108
Interquartile range (IQR)82830378

Descriptive statistics

Standard deviation41727692
Coefficient of variation (CV)0.83800518
Kurtosis-1.3186493
Mean49794074
Median Absolute Deviation (MAD)35474456
Skewness0.0098291786
Sum4.9794074 × 1011
Variance1.7412003 × 1015
MonotonicityNot monotonic
2024-12-03T10:10:10.697732image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3617
36.2%
43908203.25 1
 
< 0.1%
58930954.7 1
 
< 0.1%
75235738 1
 
< 0.1%
92601245.4 1
 
< 0.1%
73419897.36 1
 
< 0.1%
79538857.2 1
 
< 0.1%
73490164.44 1
 
< 0.1%
64156286.04 1
 
< 0.1%
124599205 1
 
< 0.1%
Other values (6374) 6374
63.7%
ValueCountFrequency (%)
0 3617
36.2%
2837823.57 1
 
< 0.1%
8023718.36 1
 
< 0.1%
10012485.6 1
 
< 0.1%
11943768.13 1
 
< 0.1%
13440288.55 1
 
< 0.1%
14627396.19 1
 
< 0.1%
15488681.29 1
 
< 0.1%
15640800.7 1
 
< 0.1%
16244345.88 1
 
< 0.1%
ValueCountFrequency (%)
180861470 1
< 0.1%
180008163.5 1
< 0.1%
175112699 1
< 0.1%
169344769 1
< 0.1%
166478454.9 1
< 0.1%
164338248.9 1
< 0.1%
154274640.5 1
< 0.1%
152799430.7 1
< 0.1%
151569766.1 1
< 0.1%
151569516.6 1
< 0.1%

CreditScore_EstimatedSalary_Interaction
Real number (ℝ)

High correlation  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65103890
Minimum8210.22
Maximum1.6980932 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-12-03T10:10:11.190816image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum8210.22
5-th percentile6244707.6
Q132418420
median63131078
Q395552684
95-th percentile1.3141902 × 108
Maximum1.6980932 × 108
Range1.6980111 × 108
Interquartile range (IQR)63134264

Descriptive statistics

Standard deviation39133029
Coefficient of variation (CV)0.60108589
Kurtosis-0.87439336
Mean65103890
Median Absolute Deviation (MAD)31602663
Skewness0.22279385
Sum6.510389 × 1011
Variance1.531394 × 1015
MonotonicityNot monotonic
2024-12-03T10:10:11.678552image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30247097.76 1
 
< 0.1%
62734956.72 1
 
< 0.1%
68425888.64 1
 
< 0.1%
57193648.14 1
 
< 0.1%
65584814.37 1
 
< 0.1%
67221485 1
 
< 0.1%
96593077.95 1
 
< 0.1%
8271621.6 1
 
< 0.1%
44874426.88 1
 
< 0.1%
37545190.5 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
8210.22 1
< 0.1%
56293.75 1
< 0.1%
59178.75 1
< 0.1%
67095.43 1
< 0.1%
68351.7 1
< 0.1%
87133.56 1
< 0.1%
89014.14 1
< 0.1%
102537.58 1
< 0.1%
119164.77 1
< 0.1%
144512.09 1
< 0.1%
ValueCountFrequency (%)
169809319.5 1
< 0.1%
169739764 1
< 0.1%
168798290.1 1
< 0.1%
168464687.5 1
< 0.1%
168297152.5 1
< 0.1%
168163263.8 1
< 0.1%
167676593 1
< 0.1%
167095167.5 1
< 0.1%
165778109.5 1
< 0.1%
165342352.6 1
< 0.1%

Tenure_NumProducts_Interaction
Real number (ℝ)

High correlation  Zeros 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6932
Minimum0
Maximum40
Zeros413
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-12-03T10:10:12.149296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q310
95-th percentile18
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.5818446
Coefficient of variation (CV)0.72555564
Kurtosis1.272033
Mean7.6932
Median Absolute Deviation (MAD)3
Skewness1.0145254
Sum76932
Variance31.156989
MonotonicityNot monotonic
2024-12-03T10:10:12.578348image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4 1051
 
10.5%
8 998
 
10.0%
6 941
 
9.4%
2 904
 
9.0%
10 709
 
7.1%
1 568
 
5.7%
3 561
 
5.6%
9 542
 
5.4%
7 517
 
5.2%
5 501
 
5.0%
Other values (15) 2708
27.1%
ValueCountFrequency (%)
0 413
 
4.1%
1 568
5.7%
2 904
9.0%
3 561
5.6%
4 1051
10.5%
5 501
5.0%
6 941
9.4%
7 517
5.2%
8 998
10.0%
9 542
5.4%
ValueCountFrequency (%)
40 4
 
< 0.1%
36 10
 
0.1%
32 4
 
< 0.1%
30 17
 
0.2%
28 5
 
0.1%
27 23
 
0.2%
24 32
 
0.3%
21 24
 
0.2%
20 235
2.4%
18 468
4.7%

Tenure_Balance_Interaction
Real number (ℝ)

High correlation  Zeros 

Distinct6108
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean381197.29
Minimum0
Maximum2007249.6
Zeros3893
Zeros (%)38.9%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-12-03T10:10:12.964241image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median241634.29
Q3690470.82
95-th percentile1195817.5
Maximum2007249.6
Range2007249.6
Interquartile range (IQR)690470.82

Descriptive statistics

Standard deviation422718
Coefficient of variation (CV)1.1089218
Kurtosis-0.28447616
Mean381197.29
Median Absolute Deviation (MAD)241634.29
Skewness0.87057062
Sum3.8119729 × 109
Variance1.7869051 × 1011
MonotonicityNot monotonic
2024-12-03T10:10:13.555627image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3893
38.9%
501540.04 1
 
< 0.1%
159397.75 1
 
< 0.1%
1153909.6 1
 
< 0.1%
224525.68 1
 
< 0.1%
872854.4 1
 
< 0.1%
1279325.88 1
 
< 0.1%
327327.99 1
 
< 0.1%
586349.2 1
 
< 0.1%
319584 1
 
< 0.1%
Other values (6098) 6098
61.0%
ValueCountFrequency (%)
0 3893
38.9%
33787.18 1
 
< 0.1%
35549.81 1
 
< 0.1%
35741.69 1
 
< 0.1%
39344.83 1
 
< 0.1%
40488.76 1
 
< 0.1%
43504.42 1
 
< 0.1%
43772.66 1
 
< 0.1%
44054.84 1
 
< 0.1%
46870.43 1
 
< 0.1%
ValueCountFrequency (%)
2007249.6 1
< 0.1%
2000408.67 1
< 0.1%
1985461 1
< 0.1%
1917756.5 1
< 0.1%
1910917.4 1
< 0.1%
1910826.27 1
< 0.1%
1861819.02 1
< 0.1%
1859666.4 1
< 0.1%
1850637 1
< 0.1%
1837684.7 1
< 0.1%

Age_Squared
Real number (ℝ)

High correlation 

Distinct70
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1624.8896
Minimum324
Maximum8464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-12-03T10:10:13.945204image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum324
5-th percentile625
Q11024
median1369
Q31936
95-th percentile3600
Maximum8464
Range8140
Interquartile range (IQR)912

Descriptive statistics

Standard deviation942.96316
Coefficient of variation (CV)0.58032445
Kurtosis4.8610635
Mean1624.8896
Median Absolute Deviation (MAD)408
Skewness1.8952596
Sum16248896
Variance889179.52
MonotonicityNot monotonic
2024-12-03T10:10:14.240897image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1369 478
 
4.8%
1444 477
 
4.8%
1225 474
 
4.7%
1296 456
 
4.6%
1156 447
 
4.5%
1089 442
 
4.4%
1600 432
 
4.3%
1521 423
 
4.2%
1024 418
 
4.2%
961 404
 
4.0%
Other values (60) 5549
55.5%
ValueCountFrequency (%)
324 22
 
0.2%
361 27
 
0.3%
400 40
 
0.4%
441 53
 
0.5%
484 84
0.8%
529 99
1.0%
576 132
1.3%
625 154
1.5%
676 200
2.0%
729 209
2.1%
ValueCountFrequency (%)
8464 2
 
< 0.1%
7744 1
 
< 0.1%
7225 1
 
< 0.1%
7056 2
 
< 0.1%
6889 1
 
< 0.1%
6724 1
 
< 0.1%
6561 4
< 0.1%
6400 3
< 0.1%
6241 4
< 0.1%
6084 5
0.1%

Balance_Squared
Real number (ℝ)

High correlation  Zeros 

Distinct6382
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7431381 × 109
Minimum0
Maximum6.2949852 × 1010
Zeros3617
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-12-03T10:10:14.517261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9.4475563 × 109
Q31.6293052 × 1010
95-th percentile2.6475089 × 1010
Maximum6.2949852 × 1010
Range6.2949852 × 1010
Interquartile range (IQR)1.6293052 × 1010

Descriptive statistics

Standard deviation9.4001714 × 109
Coefficient of variation (CV)0.96479916
Kurtosis-0.089831757
Mean9.7431381 × 109
Median Absolute Deviation (MAD)9.4475563 × 109
Skewness0.67125441
Sum9.7431381 × 1013
Variance8.8363223 × 1019
MonotonicityNot monotonic
2024-12-03T10:10:14.795923image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3617
36.2%
1.694444238 × 10102
 
< 0.1%
1.112470983 × 10102
 
< 0.1%
2.540764271 × 10101
 
< 0.1%
2.080480258 × 10101
 
< 0.1%
1.260294524 × 10101
 
< 0.1%
1.190429381 × 10101
 
< 0.1%
2.020586058 × 10101
 
< 0.1%
1.190484589 × 10101
 
< 0.1%
2.148783652 × 10101
 
< 0.1%
Other values (6372) 6372
63.7%
ValueCountFrequency (%)
0 3617
36.2%
14203024.32 1
 
< 0.1%
155231415.5 1
 
< 0.1%
203427463.8 1
 
< 0.1%
285393383.1 1
 
< 0.1%
552405581 1
 
< 0.1%
578087487.9 1
 
< 0.1%
744658411.9 1
 
< 0.1%
757193544.1 1
 
< 0.1%
770393870.6 1
 
< 0.1%
ValueCountFrequency (%)
6.294985157 × 10101
< 0.1%
5.682862876 × 10101
< 0.1%
4.940289935 × 10101
< 0.1%
4.907678148 × 10101
< 0.1%
4.670348023 × 10101
< 0.1%
4.594461926 × 10101
< 0.1%
4.543130257 × 10101
< 0.1%
4.52745624 × 10101
< 0.1%
4.523972454 × 10101
< 0.1%
4.523829949 × 10101
< 0.1%

CreditScore_Squared
Real number (ℝ)

High correlation 

Distinct460
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean432528.65
Minimum122500
Maximum722500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-12-03T10:10:15.042758image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum122500
5-th percentile239121
Q1341056
median425104
Q3515524
95-th percentile659344
Maximum722500
Range600000
Interquartile range (IQR)174468

Descriptive statistics

Standard deviation125628.72
Coefficient of variation (CV)0.29045179
Kurtosis-0.42841908
Mean432528.65
Median Absolute Deviation (MAD)86380
Skewness0.27249881
Sum4.3252865 × 109
Variance1.5782575 × 1010
MonotonicityNot monotonic
2024-12-03T10:10:15.302889image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
722500 233
 
2.3%
459684 63
 
0.6%
429025 54
 
0.5%
497025 53
 
0.5%
444889 53
 
0.5%
467856 52
 
0.5%
448900 50
 
0.5%
423801 50
 
0.5%
435600 48
 
0.5%
466489 48
 
0.5%
Other values (450) 9296
93.0%
ValueCountFrequency (%)
122500 5
0.1%
123201 1
 
< 0.1%
128164 1
 
< 0.1%
128881 1
 
< 0.1%
131769 1
 
< 0.1%
133225 1
 
< 0.1%
134689 1
 
< 0.1%
139129 1
 
< 0.1%
141376 2
 
< 0.1%
145924 1
 
< 0.1%
ValueCountFrequency (%)
722500 233
2.3%
720801 8
 
0.1%
719104 5
 
0.1%
717409 6
 
0.1%
715716 5
 
0.1%
714025 6
 
0.1%
712336 7
 
0.1%
710649 2
 
< 0.1%
708964 7
 
0.1%
707281 12
 
0.1%

Log_Balance
Real number (ℝ)

High correlation  Zeros 

Distinct6382
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4413267
Minimum0
Maximum12.432806
Zeros3617
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-12-03T10:10:15.579708image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11.484521
Q311.75701
95-th percentile11.999741
Maximum12.432806
Range12.432806
Interquartile range (IQR)11.75701

Descriptive statistics

Standard deviation5.6063999
Coefficient of variation (CV)0.75341402
Kurtosis-1.6679604
Mean7.4413267
Median Absolute Deviation (MAD)0.40941638
Skewness-0.57082849
Sum74413.267
Variance31.43172
MonotonicityNot monotonic
2024-12-03T10:10:15.867717image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3617
36.2%
11.77661055 2
 
< 0.1%
11.56622677 2
 
< 0.1%
11.9791642 1
 
< 0.1%
11.87923178 1
 
< 0.1%
11.62860709 1
 
< 0.1%
11.60009166 1
 
< 0.1%
11.8646263 1
 
< 0.1%
11.60011485 1
 
< 0.1%
11.89538326 1
 
< 0.1%
Other values (6372) 6372
63.7%
ValueCountFrequency (%)
0 3617
36.2%
8.234748049 1
 
< 0.1%
9.430294041 1
 
< 0.1%
9.565480138 1
 
< 0.1%
9.734748731 1
 
< 0.1%
10.06493909 1
 
< 0.1%
10.08765948 1
 
< 0.1%
10.21425473 1
 
< 0.1%
10.22260107 1
 
< 0.1%
10.23124226 1
 
< 0.1%
ValueCountFrequency (%)
12.43280611 1
< 0.1%
12.38165723 1
< 0.1%
12.31164197 1
< 0.1%
12.30833045 1
< 0.1%
12.28354689 1
< 0.1%
12.27535595 1
< 0.1%
12.26973829 1
< 0.1%
12.26801029 1
< 0.1%
12.2676254 1
< 0.1%
12.26760965 1
< 0.1%

Log_EstimatedSalary
Real number (ℝ)

High correlation 

Distinct9999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.208386
Minimum2.5321083
Maximum12.20604
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-12-03T10:10:16.140872image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2.5321083
5-th percentile9.195512
Q110.839642
median11.514873
Q311.914311
95-th percentile12.155602
Maximum12.20604
Range9.6739318
Interquartile range (IQR)1.0746687

Descriptive statistics

Standard deviation1.0002163
Coefficient of variation (CV)0.089238207
Kurtosis5.6415644
Mean11.208386
Median Absolute Deviation (MAD)0.4776381
Skewness-1.9986948
Sum112083.86
Variance1.0004326
MonotonicityNot monotonic
2024-12-03T10:10:16.408512image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.1236635 2
 
< 0.1%
11.70772394 1
 
< 0.1%
11.40618422 1
 
< 0.1%
11.19136501 1
 
< 0.1%
12.11556681 1
 
< 0.1%
8.516180679 1
 
< 0.1%
11.73579554 1
 
< 0.1%
11.99518135 1
 
< 0.1%
8.22440209 1
 
< 0.1%
10.92078927 1
 
< 0.1%
Other values (9989) 9989
99.9%
ValueCountFrequency (%)
2.532108251 1
< 0.1%
4.511628442 1
< 0.1%
4.529907701 1
< 0.1%
4.577490617 1
< 0.1%
4.679070994 1
< 0.1%
4.820845922 1
< 0.1%
4.968492984 1
< 0.1%
4.972171628 1
< 0.1%
5.188446695 1
< 0.1%
5.38114082 1
< 0.1%
ValueCountFrequency (%)
12.20604004 1
< 0.1%
12.20593134 1
< 0.1%
12.20584427 1
< 0.1%
12.20572343 1
< 0.1%
12.20562414 1
< 0.1%
12.20539116 1
< 0.1%
12.20536475 1
< 0.1%
12.20528393 1
< 0.1%
12.20511769 1
< 0.1%
12.20510533 1
< 0.1%

Balance_to_EstimatedSalary_Ratio
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct6384
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7901501
Minimum0
Maximum9770.8831
Zeros3617
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-12-03T10:10:16.692412image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.74699756
Q31.5140021
95-th percentile7.249045
Maximum9770.8831
Range9770.8831
Interquartile range (IQR)1.5140021

Descriptive statistics

Standard deviation100.05576
Coefficient of variation (CV)26.398891
Kurtosis9087.2039
Mean3.7901501
Median Absolute Deviation (MAD)0.74699756
Skewness93.396676
Sum37901.501
Variance10011.155
MonotonicityNot monotonic
2024-12-03T10:10:16.959911image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3617
36.2%
6.741532902 1
 
< 0.1%
1.83593438 1
 
< 0.1%
2.780637246 1
 
< 0.1%
0.7822023076 1
 
< 0.1%
8.719857018 1
 
< 0.1%
0.899389113 1
 
< 0.1%
3.599665122 1
 
< 0.1%
21.8439721 1
 
< 0.1%
1.630998193 1
 
< 0.1%
Other values (6374) 6374
63.7%
ValueCountFrequency (%)
0 3617
36.2%
0.02128406861 1
 
< 0.1%
0.0794650291 1
 
< 0.1%
0.1383662635 1
 
< 0.1%
0.1416132822 1
 
< 0.1%
0.1809953777 1
 
< 0.1%
0.1875132022 1
 
< 0.1%
0.1925807942 1
 
< 0.1%
0.2009916317 1
 
< 0.1%
0.2053776745 1
 
< 0.1%
ValueCountFrequency (%)
9770.883148 1
< 0.1%
1311.805175 1
< 0.1%
849.1642621 1
< 0.1%
607.0340169 1
< 0.1%
436.1363234 1
< 0.1%
351.8073764 1
< 0.1%
348.5070562 1
< 0.1%
344.6832824 1
< 0.1%
320.6303944 1
< 0.1%
290.6580917 1
< 0.1%

NumProducts_to_Tenure_Ratio
Real number (ℝ)

High correlation 

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36717311
Minimum0.090909091
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-12-03T10:10:17.193510image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.090909091
5-th percentile0.1
Q10.16666667
median0.25
Q30.5
95-th percentile1
Maximum3
Range2.9090909
Interquartile range (IQR)0.33333333

Descriptive statistics

Standard deviation0.3377603
Coefficient of variation (CV)0.91989387
Kurtosis10.250591
Mean0.36717311
Median Absolute Deviation (MAD)0.10714286
Skewness2.7898229
Sum3671.7311
Variance0.11408202
MonotonicityNot monotonic
2024-12-03T10:10:17.431397image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.5 1039
 
10.4%
0.25 1011
 
10.1%
0.3333333333 969
 
9.7%
0.2 955
 
9.6%
1 696
 
7.0%
0.6666666667 550
 
5.5%
0.1111111111 525
 
5.2%
0.125 517
 
5.2%
0.1666666667 501
 
5.0%
0.1 500
 
5.0%
Other values (20) 2737
27.4%
ValueCountFrequency (%)
0.09090909091 241
 
2.4%
0.1 500
5.0%
0.1111111111 525
5.2%
0.125 517
5.2%
0.1428571429 490
4.9%
0.1666666667 501
5.0%
0.1818181818 228
 
2.3%
0.2 955
9.6%
0.2222222222 468
4.7%
0.25 1011
10.1%
ValueCountFrequency (%)
3 6
 
0.1%
2 175
 
1.8%
1.5 32
 
0.3%
1.333333333 11
 
0.1%
1 696
7.0%
0.8 3
 
< 0.1%
0.75 42
 
0.4%
0.6666666667 550
5.5%
0.6 20
 
0.2%
0.5714285714 4
 
< 0.1%

BalanceCategory
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size698.9 KiB
Non-Zero Balance
6383 
Zero Balance
3617 

Length

Max length16
Median length16
Mean length14.5532
Min length12

Characters and Unicode

Total characters145532
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZero Balance
2nd rowNon-Zero Balance
3rd rowNon-Zero Balance
4th rowZero Balance
5th rowNon-Zero Balance

Common Values

ValueCountFrequency (%)
Non-Zero Balance 6383
63.8%
Zero Balance 3617
36.2%

Length

2024-12-03T10:10:17.706452image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T10:10:17.901057image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
balance 10000
50.0%
non-zero 6383
31.9%
zero 3617
 
18.1%

Most occurring characters

ValueCountFrequency (%)
a 20000
13.7%
e 20000
13.7%
o 16383
11.3%
n 16383
11.3%
10000
6.9%
B 10000
6.9%
r 10000
6.9%
Z 10000
6.9%
l 10000
6.9%
c 10000
6.9%
Other values (2) 12766
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 145532
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 20000
13.7%
e 20000
13.7%
o 16383
11.3%
n 16383
11.3%
10000
6.9%
B 10000
6.9%
r 10000
6.9%
Z 10000
6.9%
l 10000
6.9%
c 10000
6.9%
Other values (2) 12766
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 145532
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 20000
13.7%
e 20000
13.7%
o 16383
11.3%
n 16383
11.3%
10000
6.9%
B 10000
6.9%
r 10000
6.9%
Z 10000
6.9%
l 10000
6.9%
c 10000
6.9%
Other values (2) 12766
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 145532
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 20000
13.7%
e 20000
13.7%
o 16383
11.3%
n 16383
11.3%
10000
6.9%
B 10000
6.9%
r 10000
6.9%
Z 10000
6.9%
l 10000
6.9%
c 10000
6.9%
Other values (2) 12766
8.8%

RegionChurnRate
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size737.9 KiB
0.16455928165658787
5457 
0.2507153863086066
4543 

Length

Max length19
Median length19
Mean length18.5457
Min length18

Characters and Unicode

Total characters185457
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.2507153863086066
2nd row0.2507153863086066
3rd row0.2507153863086066
4th row0.2507153863086066
5th row0.2507153863086066

Common Values

ValueCountFrequency (%)
0.16455928165658787 5457
54.6%
0.2507153863086066 4543
45.4%

Length

2024-12-03T10:10:18.101520image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T10:10:18.281421image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.16455928165658787 5457
54.6%
0.2507153863086066 4543
45.4%

Most occurring characters

ValueCountFrequency (%)
6 34543
18.6%
5 30914
16.7%
8 25457
13.7%
0 23629
12.7%
1 15457
8.3%
7 15457
8.3%
. 10000
 
5.4%
2 10000
 
5.4%
3 9086
 
4.9%
9 5457
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 185457
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 34543
18.6%
5 30914
16.7%
8 25457
13.7%
0 23629
12.7%
1 15457
8.3%
7 15457
8.3%
. 10000
 
5.4%
2 10000
 
5.4%
3 9086
 
4.9%
9 5457
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 185457
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 34543
18.6%
5 30914
16.7%
8 25457
13.7%
0 23629
12.7%
1 15457
8.3%
7 15457
8.3%
. 10000
 
5.4%
2 10000
 
5.4%
3 9086
 
4.9%
9 5457
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 185457
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 34543
18.6%
5 30914
16.7%
8 25457
13.7%
0 23629
12.7%
1 15457
8.3%
7 15457
8.3%
. 10000
 
5.4%
2 10000
 
5.4%
3 9086
 
4.9%
9 5457
 
2.9%

Interactions

2024-12-03T10:09:57.442102image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:45.491559image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:49.722218image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:54.140246image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:57.531917image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:01.524116image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:06.845051image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:11.479910image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:15.032319image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:20.737836image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:24.544829image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:28.293277image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:33.478759image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:37.232785image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:41.551702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:46.482678image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:50.045730image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:53.524626image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:57.713592image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:45.650308image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:49.980843image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:54.323848image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:57.703774image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:01.791985image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:07.170773image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:11.645226image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:15.287042image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:20.920727image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:24.737485image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:28.476561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:33.656440image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:37.440086image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:41.732062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:46.656307image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:50.207572image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:53.717842image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:58.030051image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:45.834229image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:50.328164image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:54.527046image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:58.330401image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:02.121725image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:07.516097image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:11.846287image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:15.656456image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:21.135356image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:24.947747image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:28.700667image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:33.881082image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:37.653687image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:41.927314image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:46.865855image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:50.391744image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:53.904430image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:58.249314image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:46.027294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:50.604439image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:54.693501image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-12-03T10:09:14.272605image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:19.865988image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:23.720049image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:27.504513image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:32.660022image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:36.419776image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:40.065349image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:45.438380image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:49.280028image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:52.780789image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:56.336978image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:10:01.513616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:48.768575image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:53.569744image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:56.990615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:00.923482image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:05.948514image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:10.884369image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:14.475816image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:20.063851image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:23.925574image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:27.718979image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:32.873860image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:36.631525image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:40.917528image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:45.780222image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:49.489613image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:52.959873image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:56.578341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:10:01.690964image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:49.081921image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:53.761735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:57.153457image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:01.101079image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:06.164761image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:11.078818image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:14.649826image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:20.251953image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:24.120842image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:27.902568image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:33.069060image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:36.820879image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:41.116012image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:46.029387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:49.670303image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:53.130669image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:56.857414image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:10:01.874022image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:49.390230image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:53.946139image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:08:57.327947image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:01.314713image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:06.499229image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:11.262226image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:14.832761image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:20.511502image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:24.312833image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:28.094864image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:33.267218image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:37.027797image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:41.344373image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:46.270456image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:49.852419image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:53.313454image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T10:09:57.171640image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-12-03T10:10:18.463059image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
AgeAge_Balance_InteractionAge_NumProducts_InteractionAge_SquaredBalanceBalanceCategoryBalance_SquaredBalance_to_EstimatedSalary_RatioCreditScoreCreditScore_Balance_InteractionCreditScore_EstimatedSalary_InteractionCreditScore_SquaredEstimatedSalaryExitedGenderGeographyHasCrCardIsActiveMemberLog_BalanceLog_EstimatedSalaryNumOfProductsNumProducts_to_Tenure_RatioRegionChurnRateTenureTenure_Balance_InteractionTenure_NumProducts_Interaction
Age1.0000.3210.5051.0000.0330.0440.0330.036-0.0080.033-0.005-0.008-0.0020.3750.0260.0500.0130.1440.033-0.0020.087-0.0200.026-0.0100.036-0.038
Age_Balance_Interaction0.3211.000-0.0970.3210.9130.9920.9130.7970.0040.8840.0090.0040.0080.2400.0250.3090.0000.0620.9130.0080.231-0.1630.025-0.0070.769-0.154
Age_NumProducts_Interaction0.505-0.0971.0000.505-0.2590.306-0.259-0.2640.008-0.2570.0090.0080.0070.2690.0400.0270.0000.083-0.2590.0070.6590.4360.0400.005-0.2440.377
Age_Squared1.0000.3210.5051.0000.0330.0410.0330.036-0.0080.033-0.005-0.008-0.0020.3720.0400.0470.0000.1440.033-0.0020.083-0.0200.040-0.0100.036-0.038
Balance0.0330.913-0.2590.0331.0000.9981.0000.8220.0060.9550.0120.0060.0120.1410.0000.3150.0390.0141.0000.0120.230-0.1610.000-0.0100.796-0.155
BalanceCategory0.0440.9920.3060.0410.9981.0000.8900.0000.0200.9980.0170.0180.0240.1220.0000.4350.0150.0001.0000.0230.3970.1040.0000.0000.7860.210
Balance_Squared0.0330.913-0.2590.0331.0000.8901.0000.8220.0060.9550.0120.0060.0120.1250.0080.2930.0250.0021.0000.0120.207-0.1610.008-0.0100.796-0.155
Balance_to_EstimatedSalary_Ratio0.0360.797-0.2640.0360.8220.0000.8221.0000.0070.808-0.3560.007-0.3690.0240.0020.0100.0090.0010.822-0.3690.000-0.1590.002-0.0180.733-0.165
CreditScore-0.0080.0040.008-0.0080.0060.0200.0060.0071.0000.2170.2211.0000.0010.0860.0000.0180.0000.0250.0060.0010.0170.0050.0000.0010.0080.004
CreditScore_Balance_Interaction0.0330.884-0.2570.0330.9550.9980.9550.8080.2171.0000.0610.2170.0120.1220.0270.3120.0310.0000.9550.0120.230-0.1600.027-0.0100.782-0.155
CreditScore_EstimatedSalary_Interaction-0.0050.0090.009-0.0050.0120.0170.012-0.3560.2210.0611.0000.2210.9660.0000.0000.0000.0110.0000.0120.9660.0150.0040.0000.0090.0160.017
CreditScore_Squared-0.0080.0040.008-0.0080.0060.0180.0060.0071.0000.2170.2211.0000.0010.0470.0000.0170.0000.0170.0060.0010.0000.0050.0000.0010.0080.004
EstimatedSalary-0.0020.0080.007-0.0020.0120.0240.012-0.3690.0010.0120.9660.0011.0000.0000.0210.0170.0000.0250.0121.0000.0190.0030.0210.0080.0140.014
Exited0.3750.2400.2690.3720.1410.1220.1250.0240.0860.1220.0000.0470.0001.0000.1060.1730.0000.1560.1220.0080.3870.1620.1060.0220.0920.208
Gender0.0260.0250.0400.0400.0000.0000.0080.0020.0000.0270.0000.0000.0210.1061.0000.0220.0000.0200.0000.0000.0420.0401.0000.0250.0260.021
Geography0.0500.3090.0270.0470.3150.4350.2930.0100.0180.3120.0000.0170.0170.1730.0221.0000.0050.0180.3090.0100.0470.0220.0220.0280.2490.025
HasCrCard0.0130.0000.0000.0000.0390.0150.0250.0090.0000.0310.0110.0000.0000.0000.0000.0051.0000.0060.0150.0300.0000.0270.0000.0260.0000.017
IsActiveMember0.1440.0620.0830.1440.0140.0000.0020.0010.0250.0000.0000.0170.0250.1560.0200.0180.0061.0000.0210.0000.0380.0350.0200.0210.0210.019
Log_Balance0.0330.913-0.2590.0331.0001.0001.0000.8220.0060.9550.0120.0060.0120.1220.0000.3090.0150.0211.0000.0120.229-0.1610.000-0.0100.796-0.155
Log_EstimatedSalary-0.0020.0080.007-0.0020.0120.0230.012-0.3690.0010.0120.9660.0011.0000.0080.0000.0100.0300.0000.0121.0000.0110.0030.0000.0080.0140.014
NumOfProducts0.0870.2310.6590.0830.2300.3970.2070.0000.0170.2300.0150.0000.0190.3870.0420.0470.0000.0380.2290.0111.0000.3710.0420.0350.1790.521
NumProducts_to_Tenure_Ratio-0.020-0.1630.436-0.020-0.1610.104-0.161-0.1590.005-0.1600.0040.0050.0030.1620.0400.0220.0270.035-0.1610.0030.3711.0000.040-0.818-0.517-0.448
RegionChurnRate0.0260.0250.0400.0400.0000.0000.0080.0020.0000.0270.0000.0000.0210.1061.0000.0220.0000.0200.0000.0000.0420.0401.0000.0250.0260.021
Tenure-0.010-0.0070.005-0.010-0.0100.000-0.010-0.0180.001-0.0100.0090.0010.0080.0220.0250.0280.0260.021-0.0100.0080.035-0.8180.0251.0000.4050.862
Tenure_Balance_Interaction0.0360.769-0.2440.0360.7960.7860.7960.7330.0080.7820.0160.0080.0140.0920.0260.2490.0000.0210.7960.0140.179-0.5170.0260.4051.0000.199
Tenure_NumProducts_Interaction-0.038-0.1540.377-0.038-0.1550.210-0.155-0.1650.004-0.1550.0170.0040.0140.2080.0210.0250.0170.019-0.1550.0140.521-0.4480.0210.8620.1991.000

Missing values

2024-12-03T10:10:02.223832image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-03T10:10:02.894489image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CreditScoreGeographyGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedAge_Balance_InteractionAge_NumProducts_InteractionCreditScore_Balance_InteractionCreditScore_EstimatedSalary_InteractionTenure_NumProducts_InteractionTenure_Balance_InteractionAge_SquaredBalance_SquaredCreditScore_SquaredLog_BalanceLog_EstimatedSalaryBalance_to_EstimatedSalary_RatioNumProducts_to_Tenure_RatioBalanceCategoryRegionChurnRate
0619FranceFemale4220.00111101348.8810.00420.000000e+0062734956.7220.0017640.000000e+003831610.00000011.5263340.0000000.333333Zero Balance0.250715
1608SpainFemale41183807.86101112542.5803436122.26415.095518e+0768425888.64183807.8616817.023757e+0936966411.33629411.6310960.7446700.500000Non-Zero Balance0.250715
2502FranceFemale428159660.80310113931.5716705753.601268.014972e+0757193648.14241277286.4017642.549157e+1025200411.98081311.6433621.4013620.333333Non-Zero Balance0.250715
3699FranceFemale3910.0020093826.6300.00780.000000e+0065584814.3720.0015210.000000e+004886010.00000011.4492150.0000001.000000Zero Balance0.250715
4850SpainFemale432125510.8211179084.1005396965.26431.066842e+0867221485.002251021.6418491.575297e+1072250011.74015511.2782801.5870350.333333Non-Zero Balance0.250715
5645SpainMale448113755.78210149756.7115005254.32887.337248e+0796593077.9516910046.2419361.294038e+1041602511.64181811.9167740.7595990.222222Non-Zero Balance0.164559
6822FranceMale5070.0021110062.8000.001000.000000e+008271621.60140.0025000.000000e+006756840.0000009.2167000.0000000.250000Zero Balance0.164559
7376GermanyFemale294115046.74410119346.8813336355.461164.325757e+0744874426.8816460186.968411.323575e+1014137611.65310211.6897980.9639610.800000Non-Zero Balance0.250715
8501FranceMale444142051.0720174940.5006250247.08887.116759e+0737545190.508568204.2819362.017851e+1025100111.86394911.2244631.8954930.400000Non-Zero Balance0.164559
9684FranceMale272134603.8811171725.7303634304.76279.206905e+0749060399.322269207.767291.811820e+1046785611.81009911.1806191.8766210.333333Non-Zero Balance0.164559
CreditScoreGeographyGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedAge_Balance_InteractionAge_NumProducts_InteractionCreditScore_Balance_InteractionCreditScore_EstimatedSalary_InteractionTenure_NumProducts_InteractionTenure_Balance_InteractionAge_SquaredBalance_SquaredCreditScore_SquaredLog_BalanceLog_EstimatedSalaryBalance_to_EstimatedSalary_RatioNumProducts_to_Tenure_RatioBalanceCategoryRegionChurnRate
9990714GermanyMale33335016.6011053667.0801155547.80332.500185e+073.831830e+073105049.8010891.226162e+0950979610.46360610.8905740.6524660.250000Non-Zero Balance0.164559
9991597FranceFemale53488381.2111069384.7114684204.13535.276358e+074.142267e+074353524.8428097.811238e+0935640911.38942611.1474361.2737670.200000Non-Zero Balance0.250715
9992726SpainMale3620.00110195192.4000.00360.000000e+001.417097e+0820.0012960.000000e+005270760.00000012.1817460.0000000.333333Zero Balance0.164559
9993644FranceMale287155060.4111029179.5204341691.48289.985890e+071.879161e+0771085422.877842.404373e+1041473611.95157710.2812575.3138330.125000Non-Zero Balance0.164559
9994800FranceFemale2920.00200167773.5500.00580.000000e+001.342188e+0840.008410.000000e+006400000.00000012.0303760.0000000.666667Zero Balance0.250715
9995771FranceMale3950.0021096270.6400.00780.000000e+007.422466e+07100.0015210.000000e+005944410.00000011.4749290.0000000.333333Zero Balance0.164559
9996516FranceMale351057369.61111101699.7702007936.35352.960272e+075.247708e+0710573696.1012253.291272e+0926625610.95728711.5297900.5641020.090909Non-Zero Balance0.164559
9997709FranceFemale3670.0010142085.5810.00360.000000e+002.983868e+0770.0012960.000000e+005026810.00000010.6474840.0000000.125000Zero Balance0.250715
9998772GermanyMale42375075.3121092888.5213153163.02845.795814e+077.170994e+076225225.9317645.636302e+0959598411.22626011.4391660.8082220.500000Non-Zero Balance0.164559
9999792FranceFemale284130142.7911038190.7803643998.12281.030731e+083.024710e+074520571.167841.693715e+1062726411.77639510.5503763.4076130.200000Non-Zero Balance0.250715